import numpy as np
from typing import List
import heapq
import openai
# import editdistance
from gensim.summarization.bm25 import BM25
import gensim
import json
from nltk import pos_tag
# from nltk.stem import WordNetLemmatizer
# from nltk.corpus import wordnet, stopwords
# from nltk.tokenize import word_tokenize 
# import nltk



# nltk.download('stopwords')
# nltk.download('punkt')
# nltk.download('averaged_perceptron_tagger')
# nltk.download('wordnet')
# wnl = WordNetLemmatizer()

# corpus = []
# with open("/Users/4paradigm/Desktop/work/2023_05_22/root_causes_dbmind.jsonl", 'r') as f:
#     data = json.load(f)
#     corpus = [example["desc"] for example in data]
#     metrics = [example["metrics"] for example in data]
# stop_words = set(stopwords.words('english')) 

# preprocessed_corpus = []
# for c in corpus:
#     word_tokens = word_tokenize(c) 
#     preprocessed_corpus.append([wnl.lemmatize(w,pos='n') for w in word_tokens if not w in stop_words])


# def embedding(input:str):
#     response = openai.Embedding.create(
#         input=input,
#         model="text-embedding-ada-002"
#     )
#     embeddings = response['data'][0]['embedding']
#     # print("\n-----\ntext:{}\n embeddings:{}\n-----\n".format(input, embeddings))
#     return embeddings

# def euclidean_distance(target:List[float], sample:List[float]):
#     """
#     return the euclidean distance of two vectors
#     """
#     return np.sqrt(np.sum(np.square(np.asarray(target) - np.asarray(sample))))

# def cosine_distance(target:List[float], sample:List[float]):
#     """
#     return the euclidean distance of two vectors
#     """
#     return 1 - np.dot(target,sample)/(np.linalg.norm(target)*np.linalg.norm(sample))

# def linear_search(k:int, target:List[float], samples:List[List[float]]):
#     """
#     k: the top-k examples
#     target: incoming metrics
#     samples: examples
#     """
#     func_distance = cosine_distance
#     # func_distance = cosine_distance
#     dist = []
#     for s in samples:
#         dist.append(func_distance(target, s))
#     index = heapq.nlargest(k, range(len(dist)), dist.__getitem__)
#     return index

    
# THRESHOLD = 0.5

# def editdis_linear(k:int, target:List[str], samples:List[List[str]]):
#     dist = []
#     for sample in samples:
#         dis = len(target)
#         for t in target:
#             dis_samples = [editdistance.eval(t, s)/max(len(t), len(s)) for s in sample]
#             if min(dis_samples) < THRESHOLD:
#                 dis -= 1
#         dist.append(dis)
#     index = heapq.nsmallest(k, range(len(dist)), dist.__getitem__)
#     return index
    
def simple_tok(sent:str):
    return sent.split()

# def get_wordnet_pos(tag):
#     if tag.startswith('J'):
#         return wordnet.ADJ
#     elif tag.startswith('V'):
#         return wordnet.VERB
#     elif tag.startswith('N'):
#         return wordnet.NOUN
#     elif tag.startswith('R'):
#         return wordnet.ADV
#     else:
#         return None

def bm25(k, target:List[str], sample:List[List[str]]):
    tok_corpus = sample
    bm25 = BM25(tok_corpus)
    query = target
    scores = bm25.get_scores(query)

    best_docs = sorted(range(len(scores)), key=lambda i: scores[i], reverse=True)[:k]
    best_docs_none_zero = []
    for d in best_docs:
        if scores[d] != 0:
            best_docs_none_zero.append(d)
    return best_docs_none_zero


# if __name__ == "__main__":
#     nltk.download('stopwords')
#     nltk.download('punkt')
#     nltk.download('averaged_perceptron_tagger')
#     nltk.download('wordnet')
#     wnl = WordNetLemmatizer()

#     corpus = []
#     with open("/Users/4paradigm/Desktop/work/2023_05_22/root_causes_dbmind.jsonl", 'r') as f:
#         data = json.load(f)
#         corpus = [example["desc"] for example in data]
#         metrics = [example["metrics"] for example in data]
#     stop_words = set(stopwords.words('english')) 
    
#     preprocessed_corpus = []
#     for c in corpus:
#         word_tokens = word_tokenize(c) 
#         preprocessed_corpus.append([wnl.lemmatize(w,pos='n') for w in word_tokens if not w in stop_words])
#     print(preprocessed_corpus)

#     best_docs = bm25(3, "package drop rate threshold", preprocessed_corpus)
    # for i, b in enumerate(best_docs):
    #     print(f"desc {i+1}: {corpus[b]}")
    #     print(f"metrics {i+1}: {metrics[b]}")
    #     print("\n\n\n\n")








    # metrics_str = "the number of active sessions"
    # examples = [
    #     "If there are many active sessions, high-concurrency sessions can consume significant amounts of memory, especially if they execute memory-intensive operations or hold large result sets in memory. This can lead to memory pressure, increased disk I/O, and potential out-of-memory errors if the system does not have enough available memory to handle the workload.",
    #     "If there are slow queries, they involve large result sets that need to be stored in memory before being sent back to the client application. If the query returns a substantial number of rows or the result set contains large data objects (e.g., images, documents), it can consume a significant amount of memory ...",
    # ]
    # metrics_embedding = embedding(metrics_str)
    # examples_embedding = [embedding(e) for e in examples]
    # metrics_embedding = [-0.0006464981124736369, 0.0011919541284441948, 0.012529219500720501, -0.015440601855516434, -0.00987129844725132, -0.005535051692277193, -0.039101578295230865, -0.0035553115885704756, -0.0023136925883591175, -0.035566817969083786, 0.018605446442961693, 0.012700477614998817, -0.003983456175774336, -0.008234074339270592, -0.020550934597849846, -0.01579681783914566, 0.014303450472652912, -0.00839163176715374, 0.02501733787357807, -0.02645590342581272, -0.022633429616689682, -0.003805347951129079, -0.025811973959207535, -0.0012887148186564445, -0.018947960808873177, -0.015522805973887444, 0.003020987380295992, -0.00863824225962162, -7.262400322360918e-05, -0.017509395256638527, 0.011645529419183731, -0.019893303513526917, -0.016070829704403877, -0.007624396588653326, -0.0179752167314291, 0.02253752388060093, -0.0007573875482194126, -0.005202811677008867, 0.02027692086994648, 0.01091939676553011, 0.023249957710504532, -0.0035107845906168222, -0.011967494152486324, 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    # examples_embedding = [
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    # ]
    # index = linear_search(1,metrics_embedding,examples_embedding)
    # print(index)


    # str1 = "n_bad_tup"
    # str2 = "many bad tuples"
    # print(editdistance.eval('n_bad_tup', 'many_bad_tuples')/max(len(str1), len(str2)))
